Ingestion Process Research Articles

Accelerating Battery Research and Development through Data-Driven Approaches: The Battery Data Alliance's Battdata Platform

In the rapidly evolving landscape of battery research and development, the need for efficient, standardized, and automated testing processes has become increasingly apparent. Battery testing generates an immense volume of valuable electrochemical data, including critical parameters such as cell impedance, coulombic efficiency, capacity, and constant current/constant voltage (CC/CV) charging times. However, the lack of standardization and the limitations of traditional data management approaches have hindered the ability to extract meaningful insights from this wealth of data.LF Energy's Battery Data Alliance (BDA) is a collaboration between industry leaders, academic institutions, and open-source communities to drive innovation and accelerate the development of advanced battery technologies. [1] BDA has developed BattData, a user-friendly battery data management framework, that enables researchers and engineers to efficiently store, process, analyze, and visualize vast amounts of battery testing data. BattData includes the module BattDB, a time series relational database designed to handle the unique requirements of battery data storage. BattDB acts as a centralized repository, allowing seamless access and querying of specific datasets. Its optimized architecture ensures high performance and scalability, making it capable of handling the ever-growing volume of battery testing data.To streamline the data ingestion process and ensure data integrity, we introduce BattETL (Extract, Transform, Load), an open-source Python module. BattETL automates the extraction, transformation, and loading of battery cycler data into BattDB, supporting popular cycler platforms such as Maccor and Arbin. By eliminating manual data preprocessing tasks, BattETL significantly reduces the burden on researchers and minimizes the risk of errors.In this talk, we demonstrate the benefits of the same and introduce other supporting visualization, modeling, and parameter estimation tools. References Battery Data Alliance – LF Energy (https://lfenergy.org/projects/battery-data-alliance/)

Optimizing Data Ingestion in the Cloud: Leveraging AWS Technologies like AWS S3, EMR, and Glue for Cost Efficiency and Operational Scalability

In the rapidly evolving landscape of big data, the architecture of data ingestion processes significantly impacts the operational costs and efficiency of cloud environments. This paper delves into the architectural patterns and strategies that facilitate cost-effective data ingestion, with a particular focus on leveraging Amazon Web Services (AWS) technologies such as S3, EMR, and Glue. As organizations increasingly migrate to cloud-based solutions to manage voluminous data, optimizing the cost of data ingestion becomes crucial to maintain competitiveness and operational efficiency.

The importance of taste on swallowing function.

The world's population is aging. Pneumonia is the leading cause of death among the older adults, with aspiration pneumonia being particularly common. Aspiration pneumonia is caused by a decline in swallowing function. Causes can include age-related sarcopenia of swallowing muscles, cognitive decline, cerebrovascular and other diseases or even changes in individual taste preference. Currently, the main treatment approach for dysphagia is resistance training of swallowing-related muscles. This approach has not been effective and establishment of novel methods are required. In this review, we introduce and discuss the relationship between taste, taste preference, carbonation and swallowing function. Taste and preference improve swallowing function. Recently, it has been shown that a carbonated beverage that combines the functionality of a thickening agent, the appeal of taste, and the stimulation of carbonation improves swallowing function. This may be very useful in the recovery of swallowing function. It is important to note that deliciousness is based not only on taste and preference, but also on visual information such as food form. Umami taste receptors are expressed not only in taste buds but also in skeletal muscle and small intestine. These receptors may be involved in homeostasis of the amino acid metabolic network, i.e., the process of amino acid ingestion, intestine absorption, and storage in skeletal muscle. Proper stimulation of umami receptors in organs other than taste buds may help maintain nutritional status and muscle mass. Umami receptors are therefore a potential therapeutic target for dysphagia.

Feeding responses of reef-building corals provide species- and concentration-dependent risk assessment of microplastic

The negative impacts of microplastic on reef-building corals are often attributed to the feeding responses to these particles. Although reactions to and ingestion of microplastic are frequently reported, a quantitative comparison to natural particles and of the factors influencing these responses is largely missing. Thus, this study aims to compare the feeding rates of corals to microplastic and natural particles, considering factors influencing these responses. Specifically, we I) studied the feeding responses of corals to microplastic, natural food, and non-food particles, II) examined the influence of biotic factors (i.e., biofilm on the particles and presence of natural food), III) evaluated species-specific differences in feeding responses to microplastic particles, and IV) applied a toxicodynamic model for species- and concentration-dependent risk assessments. We assessed the feeding responses of 11 coral species, spanning different life-history strategies and growth forms in experimental feeding trials. The results showed that the feeding responses of corals to microplastic differ from those to naturally occurring particles. Reactions to microplastic and natural food occurred equally often, while sand was more frequently rejected. Yet, the ingestion process was much more selective, and microplastic was ingested less frequently than natural food. The presence of a biofilm and natural food had activating effects on the feeding behavior of the corals on microplastic. Generally, coral species that exhibit a higher degree of heterotrophic feeding also reacted more often to microplastic. The species- and concentration-dependent toxicodynamic risk model built on these data reveals that most tested coral species are unlikely to be at risk under present environmental concentration levels. However, highly heterotrophic feeders, such as Blastomussa merleti, or generally vulnerable species, such as Pocillopora verrucosa, need special consideration. These findings help to better evaluate the responses of corals to microplastic and their risk in an increasingly polluted ocean.

Improving Protein Therapeutic Development through Cloud-based Data Integration.

Pharma 4.0 is a digital evolution of the pharmaceutical industry that automates scientists' traditional workflows with the implementation of modern technologies like cloud pipelines, artificial intelligence, robotic platforms, and augmented reality. Lab data capture (LDC) is an essential strategy for initiating Pharma 4.0 that aggregates and harmonizes siloed lab data from analytical instruments, reporting systems, and operational platforms. This publication describes the execution of LDC within a quantitative PCR (qPCR) workflow using the Tetra Data Platform (TDP). We selected this workflow because the qPCR instrument, the ViiA7, generates discrete file-based data that documents execution of individual assays for quantifying residual DNA throughout biologics process development and product profiling. TDP executes LDC through the deployment of file scanning software agents, scanning and ingestion processes, and a cloud-based parsing pipeline that harmonizes source data. Web applications were developed to query, visualize, and interpret harmonized qPCR data for automated experiment data processing and process control charting from the TDP platform. Our implementation of LDC enables analytical researchers to harness FAIR (Findable, Accessible, Interoperable, Reproducible) data practices across the organization and establishes a "compliance-by-code" culture in development labs.

Fraud Detection System Employing Machine Learning Techniques for Credit Card Transactions

This research paper presents a comprehensive demonstration of the Microsoft Fabric data science workflow, illustrating its effectiveness through an end-to-end example centered on the development of an advanced fraud detection system. The primary objective of this study is to construct a robust and efficient fraud detection mechanism by leveraging the capabilities of machine learning algorithms trained on historical data encompassing instances of fraudulent activities. The overarching aim is to discern intricate patterns inherent in fraudulent events, thus empowering the system to swiftly and accurately identify and flag such activities in case of their recurrence. This paper expounds upon a meticulously designed workflow that encompasses a series of pivotal steps, including the installation of custom libraries tailored to the task, meticulous data loading and preprocessing, a comprehensive exploratory data analysis phase aimed at extracting meaningful insights, the intricate process of training a machine learning model using Scikit-Learn and Flow, the critical step of selecting and registering the most performant model, and, finally, the seamless deployment of the trained model for real-time scoring and prediction.The core of the presented workflow centers on the concept of a lake house, wherein the data is sourced from a public blob and subsequently stored for comprehensive analysis. This architectural paradigm underscores the significance of unified data storage, offering a coherent platform for seamless integration and manipulation. The research paper emphasizes that this approach not only elevates the efficiency of data handling but also lays the foundation for consistent and structured analysis, ultimately enhancing the accuracy and applicability of the subsequent machine learning stages.By tackling the complexities of a real-world scenario involving fraud detection, this research paper underscores the versatility and adaptability of the Microsoft Fabric framework. While the primary focus is on the development of a robust fraud detection system, the significance of this approach reverberates across diverse data science endeavors. The intricate process of custom library installation, data ingestion, preprocessing, and model deployment provides an invaluable resource for practitioners navigating the multifaceted landscape of data science workflows within the Microsoft Fabric ecosystem.In a world characterized by an exponential surge in data generation and a corresponding demand for actionable insights, the presented paper serves as more than just a solution to the specific problem of fraud detection. It morphs into a foundational template, empowering data scientists, researchers, and industry professionals with a structured approach to harnessing the potential of complex datasets. The step-by-step elucidation of the workflow equips practitioners with a tangible guide, offering insights into handling intricacies that often accompany large-scale data analysis. Through its detailed exposition, the paper bridges the gap between theory and practice, allowing readers to not only comprehend the theoretical underpinnings of the Microsoft Fabric framework but also practically implement and adapt its methodologies to suit a myriad of data science challenges. In conclusion, this research paper transcends the confines of a traditional study on fraud detection by encompassing a holistic exploration of the Microsoft Fabric data science workflow. While effectively addressing the practical challenge of developing an advanced fraud detection system, it simultaneously contributes a substantive and versatile resource for the data science community at large. As organizations across domains seek to extract maximum value from their data assets, the workflow detailed in this paper offers a beacon of guidance, ensuring that the journey from data to insights is traversed with precision and efficacy.

Arsenic Uptake, Transport, Accumulation in Rice and Prospective Abatement Strategies - A Review

Recent reports claim that arsenic (As) toxicity affects millions of individuals worldwide. A significant problem for rice output and quality as well as for human health is the high content of arsenic (As), a non-essential poisonous metalloid, in rice grains. Therefore, substantial research has been done on the interactions between rice and As in recent years. As rice plants uptake at the root surface is impacted by factors like radical oxygen loss and iron plaque. The absorption and movement of various As species as well as the transfer to sub cellular compartments include a multitude of transporters, including phosphate transporters and aquaglyceroporins. As III and AsV are transported into the root by phosphate transporters and intrinsic channels that mimic nodulin 26. The silicic acid transporter may have a substantial impact on how methylated As, dimethylarsinic acid (DMA), and monomethylarsonic acid (MMA), enter the root. The issue of As contamination in rice is being addressed by researchers and practitioners to the best of their abilities. Making better plans may be aided by recent research on rice that explains the processes of arsenic ingestion, transportation, and metabolism at the rhizosphere. Common agronomic techniques, such as collecting rainwater for agricultural irrigation, using natural substances that aid in the methylation of arsenic, and biotechnology methods, may be investigated in an effort to lessen the uptake of arsenic by food crops. Innovative agronomic techniques and recent research findings on arsenic contamination in rice crops will be included in this review.

Aptamer modified Ti3C2 nanosheets application in smart targeted photothermal therapy for cancer

BackgroundTi3C2 is a type of transition metal carbides and nitrides (MXenes) with high light-to-heat conversion efficiency property, which has been widely used in cancer treatment recently. In fact, active targeting delivery of MXenes nanomaterials with targeting molecule could enhance the therapeutic efficacy. However, targeted therapy of MXenes has not been further studied in the past. Aptamers (Apt) with excellent affinity and high specificity properties have been widely used as targeting tools. Predictably, the incorporation of Apt into Ti3C2 nanomaterials will offer an unprecedented opportunity in the research fields of cancer targeted therapy.ResultsTransmembrane glycoprotein mucin 1 (MUC1) is overexpressed on the surface of MCF-7 cells, and MUC1 Apt (Apt-M) could target MCF-7 cells with high affinity and specificity. Here, a smart targeting nanotherapeutic system Ti3C2/Apt-M was fabricated, which could specifically recognize and enter in MCF-7 cells. Benefitting from the desirable targeted performance of Apt-M, MCF-7 cells completed the ingestion process of Ti3C2/Apt-Mf nanosheets within 4 h, and Apt-M facilitated the entry of the Ti3C2/Apt-Mf nanosheets into MCF-7 cells. Besides, Ti3C2/Apt-M nanosheets exhibited the potential as an outstanding photothermal agent (PTA) because of the photothermal performance inherited from wrapped Ti3C2 nanosheets. As demonstrated, upon 808 nm laser irradiation, the Ti3C2/Apt-M nanotherapeutic system displayed a satisfactory antitumor effect by targeted photothermal therapy both in vitro and in vivo.ConclusionThis study provides a new idea for the development of MXenes nanotherapeutic system with high active targeting performance.Graphical

Streamlining PySpark Data Pipelines: Integrating External Data Sources and APIs

In the ever-expanding domain of big data analytics, integrating external data sources and APIs into data processing workflows is essential for enriching analyses and enhancing decision-making. PySpark, a prominent component of the Apache Spark ecosystem, offers robust capabilities for high-performance data processing but often requires extension to utilize external databases, web services, and third-party APIs effectively. This paper explores diverse methodologies for importing and managing data from these varied sources directly into PySpark. Techniques discussed include utilizing JDBC for database connections, employing HTTP clients for accessing RESTful APIs, and leveraging cloud storage APIs compatible with Hadoop. While integrating external data sources into PySpark pipelines significantly enriches the available data landscape, it also introduces challenges such as scalability of ingestion processes, maintenance of data integrity, and real-time data processing overhead. This study aims to provide a detailed examination of these integration techniques, highlighting both the strategic advantages and potential complications. By addressing these challenges, the paper presents a pathway towards more dynamic and comprehensive data analytics platforms, enabling businesses to leverage real-time insights and drive more nuanced analyses through PySpark.

Spatiotemporal Live-Cell Analysis of Photoreceptor Outer Segment Membrane Ingestion by the Retinal Pigment Epithelium Reveals Actin-Regulated Scission.

The photoreceptor outer segment (OS) is the phototransductive organelle in the vertebrate retina. OS tips are regularly ingested and degraded by the adjacent retinal pigment epithelium (RPE), offsetting the addition of new disk membrane at the base of the OS. This catabolic role of the RPE is essential for photoreceptor health, with defects in ingestion or degradation underlying different forms of retinal degeneration and blindness. Although proteins required for OS tip ingestion have been identified, spatiotemporal analysis of the ingestion process in live RPE cells is lacking; hence, the literature reflects no common understanding of the cellular mechanisms that affect ingestion. We imaged live RPE cells from mice (both sexes) to elucidate the ingestion events in real time. Our imaging revealed roles for f-actin dynamics and specific dynamic localizations of two BAR (Bin-Amphiphysin-Rvs) proteins, FBP17 and AMPH1-BAR, in shaping the RPE apical membrane as it surrounds the OS tip. Completion of ingestion was observed to occur by scission of the OS tip from the remainder of the OS, with a transient concentration of f-actin forming around the site of imminent scission. Actin dynamics were also required for regulating the size of the ingested OS tip, and the time course of the overall ingestion process. The size of the ingested tip is consistent with the term "phagocytosis." However, phagocytosis usually refers to engulfment of an entire particle or cell, whereas our observations of OS tip scission indicate a process that is more specifically described as "trogocytosis," in which one cell "nibbles" another cell.SIGNIFICANCE STATEMENT The ingestion of the photoreceptor outer segment (OS) tips by the retinal pigment epithelium (RPE) is a dynamic cellular process that has fascinated scientists for 60 years. Yet its molecular mechanisms had not been addressed in living cells. We developed a live-cell imaging approach to investigate OS tip ingestion, and focused on the dynamic participation of actin filaments and membrane-shaping BAR proteins. We observed scission of OS tips for the first time, and were able to monitor local changes in protein concentration preceding, during, and following scission. Our approach revealed that actin filaments were concentrated at the site of OS scission and were required for regulating the size of the ingested OS tip and the time course of the ingestion process.

Efficient immediate-access dynamic indexing

In a dynamic retrieval system, documents must be ingested as they arrive, and be immediately findable by queries. Our purpose in this paper is to describe an index structure and processing regime that accommodates that requirement for immediate access, seeking to make the ingestion process as streamlined as possible, while at the same time seeking to make the growing index as small as possible, and seeking to make term-based querying via the index as efficient as possible. We describe a new compression operation and a novel approach to extensible lists which together facilitate that triple goal. In particular, the structure we describe provides incremental document-level indexing using as little as two bytes per posting and only a small amount more for word-level indexing; provides fast document insertion; supports immediate and continuous queryability; provides support for fast conjunctive queries and similarity score-based ranked queries; and facilitates fast conversion of the dynamic index to a “normal” static compressed inverted index structure. Measurement of our new mechanism confirms that in-memory dynamic document-level indexes for collections into the gigabyte range can be constructed at a rate of two gigabytes/minute using a typical server architecture, that multi-term conjunctive Boolean queries can be resolved in just a few milliseconds each on average even while new documents are being concurrently ingested, and that the net memory space required for all of the required data structures amounts to an average of as little as two bytes per stored posting, less than half the space required by the best previous mechanism.

Waterflood Optimization Advisory System Provides Insights Into Efficiency

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 207946, “Intelligent Waterflood Optimization Advisory System: A Step Change Toward Digital Transformation,” by Samat Ramatullayev, SPE, Muzahidin M. Salim, and Muhammad Ibrahim, SPE, Schlumberger, et al. The paper has not been peer reviewed. _ In the complete paper, the authors discuss the development of an end-to-end waterflooding-optimization system that provides monitoring and surveillance dashboards with artificial-intelligence (AI) and machine-learning (ML) components to generate and assess insights into operational efficiency in an automated manner. The system allows for fast screening of waterflooding performance at diverse levels, enabling prompt identification of opportunities for immediate uptake into an opportunity-management process and for evaluation in an AI-driven production forecast or a reservoir simulator. Monitoring and Surveillance Dashboards The system consists of a series of dashboards covering both subsurface and surface elements. The production data foundation, the data-management platform, can connect to multiple data sources, simplifying data-preparation and -ingestion processes. The intelligent monitoring and surveillance section consists of monitoring and surveillance dashboards focusing on six key indicators: production scorecard, injection scorecard, injection efficiency, voidage replacement ratio, injection-water quality, advanced analytics, and opportunity management. The system consists of different modules such as Field, Reservoir, Sector, Pattern, Offset Wells, Surface Facilities, and Advanced Analytics. Although each module contains some automation aspects, the Advanced Analytics module houses most of the automation and ML. The modules are detailed in the complete paper.

Building a 360-Degree Customer View with Salesforce Data Cloud

Attaining a 360-degree view of a customer is crucial for any contemporary organization which is seeking to improve the customer touchpoints and make better decisions. Salesforce Data Cloud helps organizations by amalgamating and processing data from different sources to form a single, usable customer profile. This paper aims at discussing methodology of creating a 360-degree customer view using the Salesforce Data Cloud platform, where the main focus is paid to the intricate data ingestion processes, transformation, warehousing, and analysis. It also offers a step by step A to Z analysis of the structure of the system with clear flow charts, pseudocodes and diagrams depicting the stages of data flow. Furthermore, the paper provides insights into future directions such as incorporation of AI/ML, enhanced data security and real-time processing of data. Through the use of Salesforce Data Cloud, all the entities involved can benefit from credible information that can enhance not only customer engagement, but also bolster decision making processes

Predation on the common marmoset Callithrix jacchus (Primates, Callitrichidae) by the common boa Boa constrictor (Squamata, Boidae) in the Atlantic Forest of Northeastern Brazil

Herein, we present the first record of predation on the common marmoset (Callithrix jacchus) by a common boa (Boa constrictor). The incident occurred during the afternoon in a restinga environment of the Atlantic Forest of northeastern Brazil. The snake captured the marmoset on the ground when a group of seven individuals of Callithrix jacchus was traveling among the restinga trees. The group remained on the top of a tree, from 1.5 to 4 m, exhibiting an alarm vocalization and surrounding the snake throughout the ingestion process. The snake was captured, measured, and released on the same site. This case report reinforces that the callitrichids are vulnerable to predation by large snakes and present some defensive behavior.

Occurrences, Sources, and Human Health Risk Assessments of Polycyclic Aromatic Hydrocarbons in Marine Organisms From Temperate Coastal Area

The pollution characteristics of 16 polycyclic aromatic hydrocarbons (PAHs) in marine organism species (6 species of fishes and 2 species of crustaceans) from the coastal area of the East China Sea were determined. The concentrations of 16 PAHs in the studied organisms ranged from 29.73 to 87.02 ng/g dw and 2- and 3-ring PAHs were the most abundant compounds in the aquatic organisms. The habitat, diet and predator-prey relationship have posed potential effects on the PAH accumulation in marine organisms. The source identification of PAHs was performed by using the molecular diagnostic ratios and principal component analysis (PCA). The results showed that the main sources of PAHs in the marine organisms were coal combustion, followed by mixture of gasoline combustion, oil combustion, crude oil spill and vehicle emissions. The incremental lifetime cancer risk of human via ingestion process of marine organisms in this sea area was also estimated and the assessment showed that it posed an acceptable but non-negligible risk to human health.

Examining differentiation of sympatricSchizothoraxfishes reveals low differentiation in internal compared to external feeding traits

AbstractIn the process of sympatric species differentiation, which feeding traits are most differentiated? The complete feeding process of capture, ingestion, and digestion requires certain external, as well as internal traits. Therefore, comprehensive comparisons are needed to study fish feeding habits. This study systematically investigated the adaptive radiation and parallel evolution ofSchizothoraxfishes, and quantified the external, frame, pharyngeal bone, and digestive traits of the systematically differentiated omnivorousSchizothorax davidiand herbivorousS. wangchiachii. A member of their sister genus, the carnivorousPercocypris pingi, was used as an outgroup control to explore the feeding differentiation of sympatricSchizothoraxfishes. The results showed that these species had highly differentiated external traits, while the internal traits (pharyngeal bone and digestive traits) showed low differentiation, indicating that predatory traits diverged first in the differentiation of sympatricSchizothoraxfishes, while chewing and digestive traits were slower to diverge. Furthermore, we screened 16 continuous feeding traits, and the quantified values of these traits progressively increased or decreased with increasing nutritional level of food. These findings provide a reference for the selection of quantitative indicators in future studies of sympatric species differentiation.

Implementing Efficient Data Operations: An Innovative Approach (Part -1)

In today’s data-driven environment, efficient data operations are essential for organizations to optimize performance, enhance data accuracy, and enable rapid decision-making. This paper presents an innovative approach to implementing an automated data ingestion and processing framework designed to streamline repetitive tasks, ensure data quality, and support scalability within complex data ecosystems. The approach centers on a multi-step process that integrates robotic process automation (RPA), serverless computing, and advanced data transformation algorithms, thereby reducing manual interventions and accelerating data integration from multiple sources. The data ingestion process initiates with the identification and automation of repetitive data collection tasks through RPA, effectively reducing the time and potential human error associated with manual operations. Subsequently, serverless computing and platforms such as Alteryx are utilized to integrate data from diverse sources into a unified true-source repository, following either ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) workflows. This integration facilitates seamless data transformation and mapping, applying business logic and best practices to ensure alignment with organizational data standards. Automated quality monitoring is established post-ingestion to maintain high data quality, deploying event-driven triggers to detect anomalies, validate data integrity, and promptly notify relevant stakeholders of any irregularities. The technology stack supporting this framework includes Snowflake, AWS Redshift, and Azure Data Storage, along with relational databases like SQL Server and MySQL. These tools are selected for their robust processing capabilities and scalability, addressing challenges such as real-time data processing and storage requirements. Additionally, thorough documentation and version control are maintained to capture process updates and ensure a reliable knowledge base for future iterations. Implementing this approach led to an 88% improvement in data accuracy and reliability for service and manufacturing operations, underscoring the importance of proactive decision-making, end-to-end validation checks, and cross-departmental collaboration on a unified data platform. This paper discusses the methodologies, technologies, and best practices applied in each stage of the data engineering process, as well as strategies to overcome common challenges in data quality, scalability, and pipeline integration. The findings and insights presented here offer a comprehensive framework for organizations seeking to enhance their data operations through automation, efficient resource utilization, and continuous monitoring.

Potential inhibitors for peroxiredoxin 6 of W. bancrofti: A combined study of modelling, structure-based drug design and MD simulation

Lymphatic filariasis (LF), a mosquito-borne parasitic disease caused by nematode Wuchereria bancrofti in tropical and sub-tropical countries. These nematodes are transferred into the human host when the infected mosquito carrying L3 larvae is released into the bloodstream during the blood ingestion process. The host immune system produces ROS (Reactive Oxygen Species) as a primary defence mechanism to remove the invading filarial worms. However, well-defined antioxidant enzymes of the nematodes scavenge the host-produced ROS to escape from oxidative stress. The enzyme peroxiredoxin 6 (Prx6) belongs to the peroxiredoxin family, catalyses hydrogen peroxide (H2O2) into water (H2O). In order to find the inhibitors that inhibit the activity of peroxiredoxin 6 of W. bancrofti. We performed the homology modelling to predict the WbPrx6 three-dimensional structure using the Schrödinger-Prime and the dynamic stability of the modelled WbPrx6 was analyzed by carrying out the molecular dynamic (MD) simulation for the time scale of 200ns. Further, the structure-based virtual screening shortlisted the hit molecules from the ChemBridge database based on the glide score. The potential lead molecules (ID: 10239274, 11112883, 79879205, 58160895, and 42133744) that have better binding and satisfied the ADMET properties were selected for further complex simulation and DFT calculations. The identified compounds interact with the N-terminal region of the thioredoxin domain, which plays a key role in reducing phospholipase A2 activity. Interestingly, upon binding the lead molecule, the fluctuation of the loop region that connects α-IV with the β-VI plays a vital role in affecting the geometry of the active site, which in turn affects the activity WbPrx6. The outcomes of the present computational studies could help in future drug development and designing of the effective candidate to control Lymphatic filariasis.

Polycyclic aromatic hydrocarbon contamination along roads based on levels on vehicle window films.

Vehicular emissions are known to be major contributors of airborne polycyclic aromatic hydrocarbons (PAHs) in cities. In order to assess the long-term contamination of PAHs along roads, we collected organic films from vehicle windows (26 private cars and 4 buses, in Shanghai, China) and used mathematical models to convert the film-bound PAH concentrations to the airborne PAH concentrations. The field measurements of airborne PAHs revealed that the partitioning and Level III fugacity model was suitable to estimate the airborne concentrations of high and low volatile PAHs (expect for naphthalene), respectively. The total airborne PAH concentrations along roads in Shanghai ranged from 0.83 to 3.37μgm-3 and the incremental lifetime cancer risks (ILCRtotal) by exposure to PAHs along roads were greater than the USEPA lower guideline of 10-6, indicating non-negligible carcinogenic risks to drivers and passengers, especially via ingestion processes. This study provided a practicable method to investigate long-term air contamination of PAHs in vehicles and along roads based on film-bound PAH on vehicle windows. In addition, it was also possible to investigate the health risk in vehicles as a result of exposure to PAHs. Comparisons of PAHs between roads and shipping lanes also facilitated the delineation of vehicular and shipping PAH inventories. A capsule that summarizes the main finding of the work: Investigating film-bound PAH on vehicle windows is a practicable pathway to investigate the long-term contamination of PAHs in vehicles and along roads. This method can not only simplify the sampling processes, but the model calculations. The results also enabled investigations into ILCR in vehicles and specified source apportionment of traffic PAHs.

SAVIME: An Array DBMS for Simulation Analysis and ML Models Prediction

Limitations in current DBMSs prevent their wide adoption in scientific applications. In order to make them benefit from DBMS support, enabling Declarative data analysis and visualization over scientific data, we present an in-memory array DBMS system called SAVIME. In this work we describe the system SAVIME, along with its data model. Our preliminary evaluation show how SAVIME, by using a simple storage definition language (SDL) can outperform the state-of-the-art array database system, SciDB, during the process of data ingestion. We also show that it is possible to use SAVIME as a storage alternative for a numerical solver without affecting its scalability, making it useful for modern ML based applications.